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Artificial Neural Network and its Applications in the Energy Sector An Overview

Author

Listed:
  • Damilola Elizabeth Babatunde

    (Department of Chemical Engineering, Covenant University, Otta, Ogun State, Nigeria.)

  • Ambrose Anozie

    (Department of Chemical Engineering, Covenant University, Otta, Ogun State, Nigeria.)

  • James Omoleye

    (Department of Chemical Engineering, Covenant University, Otta, Ogun State, Nigeria.)

Abstract

In order to realize the goal of optimal use of energy sources and cleaner environment at a minimal cost, researchers; field professionals; and industrialists have identified the expediency of harnessing the computational benefits provided by artificial intelligence techniques. This article provides an overview of artificial intelligence (AI), chronological blueprints of the emergence of artificial neural networks (ANNs) and some of its applications in the energy sector. This short survey reveals that despite the initial hiccups at the developmental stages of artificial neural networks, ANN has tremendously evolved, is still evolving and have been found to be effective in handling highly complex problems even in the areas of modeling, control, and optimization, to mention a few.

Suggested Citation

  • Damilola Elizabeth Babatunde & Ambrose Anozie & James Omoleye, 2020. "Artificial Neural Network and its Applications in the Energy Sector An Overview," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 250-264.
    • Handle: RePEc:eco:journ2:2020-02-31
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    References listed on IDEAS

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    Cited by:

    1. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    2. Xiaoyan Peng & Xin Guan & Yanzhao Zeng & Jiali Zhang, 2024. "Artificial Intelligence-Driven Multi-Energy Optimization: Promoting Green Transition of Rural Energy Planning and Sustainable Energy Economy," Sustainability, MDPI, vol. 16(10), pages 1-20, May.
    3. Ivan Brandić & Lato Pezo & Neven Voća & Ana Matin, 2024. "Biomass Higher Heating Value Estimation: A Comparative Analysis of Machine Learning Models," Energies, MDPI, vol. 17(9), pages 1-11, April.

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    More about this item

    Keywords

    artificial neural networks; energy sector; optimization;
    All these keywords.

    JEL classification:

    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment

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